Heterogeneous Graph Transformer based Edge Anomalous Transaction Detection with XAI for Anti-Money Laundering System

Authors

  • Mudit Agarwal

Keywords:

Anti-Money Laundering, Deep Learning, HGT, anomalous transaction, XAI.

Abstract

Money laundering is a financial crime and it is a critical challenge confronted by the banking sector. Standard Anti-Money Laundering (AML) systems are based on rules or node-based classification, which cannot deal with multi-entity relationship. This work overcomes this issue by presenting an anomalous

References

ICAI, (2015). "Importance of Anti-Money Laundering Measures and Effective KYC in Financial Transactions." kb.icai.org. Available at:

https://kb.icai.org/pdfs/PDFFile5b28c97d06d877.47667992.pdf (Accessed: 8 April 2025).

Le Khac, N. A., & Kechadi, M. T. (2010). "Application of Data Mining for Anti-Money Laundering Detection: A Case Study." Proceedings -IEEE Int. Conf. Data Mining, ICDM, 577–584.

https://doi.org/10.1109/ICDMW.2010.66.

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Published

2023-03-20

How to Cite

Mudit Agarwal. (2023). Heterogeneous Graph Transformer based Edge Anomalous Transaction Detection with XAI for Anti-Money Laundering System. Journal of Computational Analysis and Applications (JoCAAA), 31(3), 609–616. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3062

Issue

Vol. 31 No. 3 (2023): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.